Professional-Cloud-DevOps-Engineer Real Questions | Professional-Cloud-DevOps-Engineer Exam Question

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Google Professional-Cloud-DevOps-Engineer Exam Syllabus Topics:

Section	Objectives
Topic 1: Optimize performance and continuous delivery	- Monitoring and observability 1. Cloud Monitoring and Logging 2. Performance tuning and feedback loops - System performance optimization 1. Scaling strategies and load handling
Topic 2: Implement security and compliance	- Compliance and governance 1. Audit logging and policy enforcement - Secure CI/CD pipelines 1. Secret and credential management 2. IAM and least privilege access
Topic 3: Develop and implement CI/CD pipelines	- Automate build, test, and deployment processes 1. Release management practices 2. Deployment automation strategies - Build and manage CI/CD pipelines using Google Cloud tools 1. Artifact repository management 2. Cloud Build pipeline design
Topic 4: Implement site reliability engineering (SRE) practices	- Incident management and postmortems 1. Alerting and on-call practices - Define and manage SLI, SLO, and SLA 1. Error budgets and monitoring strategies

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Google Cloud Certified - Professional Cloud DevOps Engineer Exam Sample Questions (Q126-Q131):

NEW QUESTION # 126
Your team has recently deployed an NGINX-based application into Google Kubernetes Engine (GKE) and has exposed it to the public via an HTTP Google Cloud Load Balancer (GCLB) ingress. You want to scale the deployment of the application's frontend using an appropriate Service Level Indicator (SLI). What should you do?

A. Configure the vertical pod autoscaler in GKE and enable the cluster autoscaler to scale the cluster as pods expand.
B. Configure the horizontal pod autoscaler to use the average response time from the Liveness and Readiness probes.
C. Install the Stackdriver custom metrics adapter and configure a horizontal pod autoscaler to use the number of requests provided by the GCLB.
D. Expose the NGINX stats endpoint and configure the horizontal pod autoscaler to use the request metrics exposed by the NGINX deployment.

Answer: A

NEW QUESTION # 127
You recently noticed that one Of your services has exceeded the error budget for the current rolling window period. Your company's product team is about to launch a new feature. You want to follow Site Reliability Engineering (SRE) practices.
What should you do?

A. Look through other metrics related to the product and find SLOs with remaining error budget. Reallocate the error budgets and allow the feature launch.
B. Notify the team about the lack of error budget and ensure that all their tests are successful so the launch will not further risk the error budget.
C. Notify the team that their error budget is used up. Negotiate with the team for a launch freeze or tolerate a slightly worse user experience.
D. Escalate the situation and request additional error budget.

Answer: C

Explanation:
The correct answer is
A, Notify the team that their error budget is used up. Negotiate with the team for a launch freeze or tolerate a slightly worse user experience.
According to the Site Reliability Engineering (SRE) practices, an error budget is the amount of unreliability that a service can tolerate without harming user satisfaction1. An error budget is derived from the service-level objectives (SLOs), which are the measurable goals for the service quality2. When a service exceeds its error budget, it means that it has violated its SLOs and may have negatively impacted the users. In this case, the SRE team should notify the product team that their error budget is used up and negotiate with them for a launch freeze or a lower SLO3. A launch freeze means that no new features are deployed until the service reliability is restored. A lower SLO means that the product team accepts a slightly worse user experience in exchange for launching new features. Both options require a trade-off between reliability and innovation, and should be agreed upon by both teams.
The other options are incorrect because they do not follow the SRE practices. Option B is incorrect because it violates the principle of error budget autonomy, which means that each service should have its own error budget and SLOs, and should not borrow or reallocate them from other services4. Option C is incorrect because it does not address the root cause of the error budget overspend, and may create unrealistic expectations for the service reliability. Option D is incorrect because it does not prevent the possibility of introducing new errors or bugs with the feature launch, which may further degrade the service quality and user satisfaction.
Reference:
Error Budgets, Error Budgets. Service Level Objectives, Service Level Objectives. Error Budget Policies, Error Budget Policies. Error Budget Autonomy, Error Budget Autonomy.

NEW QUESTION # 128
You support an application running on App Engine. The application is used globally and accessed from various device types. You want to know the number of connections. You are using Stackdriver Monitoring for App Engine. What metric should you use?

A. flex/connections/current
B. tcp_ssl_proxy/open_connections
C. tcp_ssl_proxy/new_connections
D. (lex/jnstance/connections/current

Answer: A

NEW QUESTION # 129
You are building and deploying a microservice on Cloud Run for your organization Your service is used by many applications internally You are deploying a new release, and you need to test the new version extensively in the staging and production environments You must minimize user and developer impact. What should you do?

A. Deploy a new environment with the green tag to use as the staging environment Deploy the new version of the service to the green environment and test the new version If the tests pass, send all traffic to the green environment and delete the existing staging environment Repeat for the production environment
B. Deploy the new version of the service to the staging environment Split the traffic, and allow 1 % of traffic through to the latest version Test the latest version If the test passes gradually roll out the latest version to the staging and production environments
C. Deploy the new version of the service to the staging environment Split the traffic, and allow 50% of traffic through to the latest version Test the latest version If the test passes, send all traffic to the latest version Repeat for the production environment
D. Deploy the new version of the service to the staging environment with a new-release tag without serving traffic Test the new-release version If the test passes; gradually roll out this tagged version Repeat for the production environment

Answer: D

Explanation:
The best option for deploying a new release of your microservice on Cloud Run and testing it extensively in the staging and production environments with minimal user and developer impact is to deploy the new version of the service to the staging environment with a new-release tag without serving traffic, test the new-release version, and if the test passes, gradually roll out this tagged version. A tag is a label that you can assign to a revision of your service on Cloud Run. You can use tags to create different versions of your service without affecting traffic. You can also use tags to gradually roll out traffic to a new version of your service by using traffic splitting. This way, you can test your new release extensively in both environments and minimize user and developer impact.

NEW QUESTION # 130
You manage several production systems that run on Compute Engine in the same Google Cloud Platform (GCP) project. Each system has its own set of dedicated Compute Engine instances. You want to know how must it costs to run each of the systems. What should you do?

A. Assign all instances a label specific to the system they run. Configure BigQuery billing export and query costs per label.
B. Enrich all instances with metadata specific to the system they run. Configure Stackdriver Logging to export to BigQuery, and query costs based on the metadata.
C. Name each virtual machine (VM) after the system it runs. Set up a usage report export to a Cloud Storage bucket. Configure the bucket as a source in BigQuery to query costs based on VM name.
D. In the Google Cloud Platform Console, use the Cost Breakdown section to visualize the costs per system.

Answer: C

NEW QUESTION # 131
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